A document image is usually made up of several segments like segments in text form and segments in pictorial form (images). Previous research mainly focused on extraction of text segments from the document images using Optical character recognition OCR by thresholding the grayscale image as text is often assumed to be printed in black on a white background.
OCR is a field of research in pattern recognition, artificial intelligence and machine vision. Though academic research in this field continues, the focus on OCR has shifted to the implementation of proven techniques.
The problem which existing systems are facing is concerned with partitioning an image into multiple regions (image segments) according to some homogeneity criterion, where the main problem arises in discrimination of the text areas from the figures (half-tones), both of them being relatively easy to extract from a white background.
U.S. Pat. No. 6,473,522 B1 discloses a method for segmentation of text in images where the image may be still or in motion such as video or web pages. The method comprises receiving a digital image including text and background, the received image is quantized to reduce the number of colours and define the image in terms of certain colours only, and a text colour histogram is obtained from several portions of the text and the background. This document doesn't provide any method for segmentation of images.
U.S. Pat. No. 5,933,524 titled ‘Method for segmentation of digital colour images’ published on Mar. 8, 1999 discloses a method where the coloured objects are represented by digitized colour histograms which are used for the segmentation of digital colour images. The binary values of the colour histograms decide whether the pixel can belong to a given object segment.
Similarly, the U.S. Pat. No. 6,173,077 titled ‘Image Segmentation’ published on Sep. 1, 2001 discloses a segmentation apparatus for assigning image pixels to regions, in accordance with a predetermined criterion.
For the segmentation methods implemented in the abovementioned two patents, the extraction of the embedded text or image from the complex colour images (For e.g.—CD covers, advertisements etc.), which often use fancy font styles, different languages, uneven text size and different orientation, becomes very difficult.
Therefore, there is felt a need for a document image segmentation system which is adapted to:                perform the image segmentation on any kind of complex coloured image;        capture all important regions and groups present in the document image;        extract text from any complex colour image, wherein the text can be uniform or has uneven text size such as fancy font styles, calligraphic styles or having different orientations;        extract various image parts present in the image; and        find the image segments efficiently.        